Generate a random data matrix with or without proteomics, log-transformed feature intensity-like properties.
character indicating one of the three different type of models:
number of rows of data matrix (only for
number of columns of data matrix
logical inidicating whether data properties are plot to
figure (only for
"rand", each matrix element is drawn from a
standard normal distribution N(0,1). For model
matrix elements of each row are drawn from a Gaussian distribution
N(μ_i,σ_i^2) where the mean and standard deviation itself are
drawn Gaussian distributions, i.e. σ_i~N(0,0.0625) and
μ_i~N(28,4). About 35\
to the missing value pattern present in real protein LFQ
intensities. For model
"omics.dep", a single differentially epxressed
RI feature is stacked on top of the matrix from model
matrix of size nrow x ncol.
Brombacher, E., Schad, A., Kreutz, C. (2020). Tail-Robust Quantile Normalization. BioRxiv.
example_NApattern() for description of missing value pattern.
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